Задание 1. Загрузите датасет
insurance_cost.csv
library(readr)
inc <- read_csv("C:/R/insurance_cost.csv")
## Rows: 1338 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): sex, smoker, region
## dbl (4): age, bmi, children, charges
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(inc)
## # A tibble: 6 × 7
## age sex bmi children smoker region charges
## <dbl> <chr> <dbl> <dbl> <chr> <chr> <dbl>
## 1 19 female 27.9 0 yes southwest 16885.
## 2 18 male 33.8 1 no southeast 1726.
## 3 28 male 33 3 no southeast 4449.
## 4 33 male 22.7 0 no northwest 21984.
## 5 32 male 28.9 0 no northwest 3867.
## 6 31 female 25.7 0 no southeast 3757.
#summary(inc)
#skim(inc)
Задание 2. Сделайте интерактивный plotly график
отношения индекса массы тела и трат на страховку. Раскрасьте его по
колонке smoker
library(plotly)
## Загрузка требуемого пакета: ggplot2
##
## Присоединяю пакет: 'plotly'
## Следующий объект скрыт от 'package:ggplot2':
##
## last_plot
## Следующий объект скрыт от 'package:stats':
##
## filter
## Следующий объект скрыт от 'package:graphics':
##
## layout
plot_ly(data = inc,
x = ~ bmi,
y = ~ charges,
color = ~smoker)
## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter
## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
Задание 3. Сделайте тоже самое через ggplotly
plot <- inc %>%
ggplot(aes(x = bmi,
y = charges,
color = smoker)) +
geom_point(size = 1) +
theme_minimal()
ggplotly(plot)
Задание 4. Кратко сделайте корреляционный анализ
данных insurance_cost. Посмотрите документацию пакетов, которые мы
проходили на занятии и, исходя из этого, постройте минимум два новых
типа графика (которые мы не строили на занятии)
library(corrplot)
## corrplot 0.92 loaded
inc_clear <- inc %>%
select(is.integer | is.numeric)
## Warning: Predicate functions must be wrapped in `where()`.
##
## # Bad
## data %>% select(is.integer)
##
## # Good
## data %>% select(where(is.integer))
##
## ℹ Please update your code.
## This message is displayed once per session.
## Warning: Predicate functions must be wrapped in `where()`.
##
## # Bad
## data %>% select(is.numeric)
##
## # Good
## data %>% select(where(is.numeric))
##
## ℹ Please update your code.
## This message is displayed once per session.
inc_cor <- cor(inc_clear)
corrplot(inc_cor, method = 'circle')
corrplot(inc_cor, method = 'ellipse')
Задание 5. Превратите все номинативные переменные в
бинарные/дамми. Т.е. sex и smoker должны стать бинарными (1/0), а каждое
уникальное значение region – отдельной колонкой, где 1 говорит о наличии
этого признака для наблюдения, а 0 – об отсутствии. Создайте новый
датафрейм, где вы оставите только нумерические переменные.
inc_new <- inc %>%
mutate(region_n = factor(region, labels = c("1","2","3","4")))
inc_new$sex <- ifelse(inc_new$sex == "female", 1, 0)
inc_new$smoker <- ifelse(inc_new$smoker == "yes", 1, 0)
inc_new$region_n <- as.numeric(inc_new$region_n)
inc_new$number <- c(1:1338)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ tibble 3.1.7 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ purrr 0.3.4 ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks plotly::filter(), stats::filter()
## ✖ dplyr::lag() masks stats::lag()
library(dplyr)
library(data.table)
##
## Присоединяю пакет: 'data.table'
##
## Следующие объекты скрыты от 'package:dplyr':
##
## between, first, last
##
## Следующий объект скрыт от 'package:purrr':
##
## transpose
library(tidyr)
inc_wider <- inc_new %>%
arrange(number) %>%
mutate(id = rowid(number)) %>%
pivot_wider(names_from = region, values_from = region_n) %>%
select(-id)
inc_wider$number <- NULL
inc_wider <- as.data.frame(inc_wider)
inc_wider$southwest <- as.numeric(unlist(inc_wider$southwest))
inc_wider$southeast <- as.numeric(unlist(inc_wider$southeast))
inc_wider$northwest <- as.numeric(unlist(inc_wider$northwest))
inc_wider$northeast <- as.numeric(unlist(inc_wider$northeast))
inc_wider[is.na(inc_wider)] <- 0
inc_wider <- inc_wider %>%
mutate(across(ends_with("st"), ~ ifelse(.>0,1,0)))
head(inc_wider)
## age sex bmi children smoker charges southwest southeast northwest
## 1 19 1 27.900 0 1 16884.924 1 0 0
## 2 18 0 33.770 1 0 1725.552 0 1 0
## 3 28 0 33.000 3 0 4449.462 0 1 0
## 4 33 0 22.705 0 0 21984.471 0 0 1
## 5 32 0 28.880 0 0 3866.855 0 0 1
## 6 31 1 25.740 0 0 3756.622 0 1 0
## northeast
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
Задание 6. Постройте иерархическую кластеризацию на
этом датафрейме
library(factoextra)
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
inc_clear_dist <- dist(inc_wider, method = "euclidean")
as.matrix(inc_clear_dist)[1:6,1:6]
## 1 2 3 4 5 6
## 1 0.000 15159.373 12435.4668 5099.569 13018.0755 13128.3082
## 2 15159.373 0.000 2723.9289 20258.927 2141.3549 2031.1273
## 3 12435.467 2723.929 0.0000 17535.013 582.6445 692.8921
## 4 5099.569 20258.927 17535.0127 0.000 18117.6165 18227.8495
## 5 13018.075 2141.355 582.6445 18117.616 0.0000 110.2964
## 6 13128.308 2031.127 692.8921 18227.849 110.2964 0.0000
inc_clear_hc <- hclust(d = inc_clear_dist,
method = "ward.D2")
fviz_dend(inc_clear_hc,
cex = 0.1)
## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.
Задание 7. Используя документацию или предложенный
учебник сделайте ещё несколько возможных графиков по иерархической
кластеризации. Попробуйте раскрасить кластеры разными цветами
#Comparing dendrograms
library(dendextend)
##
## ---------------------
## Welcome to dendextend version 1.16.0
## Type citation('dendextend') for how to cite the package.
##
## Type browseVignettes(package = 'dendextend') for the package vignette.
## The github page is: https://github.com/talgalili/dendextend/
##
## Suggestions and bug-reports can be submitted at: https://github.com/talgalili/dendextend/issues
## You may ask questions at stackoverflow, use the r and dendextend tags:
## https://stackoverflow.com/questions/tagged/dendextend
##
## To suppress this message use: suppressPackageStartupMessages(library(dendextend))
## ---------------------
##
## Присоединяю пакет: 'dendextend'
## Следующий объект скрыт от 'package:data.table':
##
## set
## Следующий объект скрыт от 'package:stats':
##
## cutree
res.dist <- dist(inc_wider, method = "euclidean")
hc1 <- hclust(res.dist, method = "average")
hc2 <- hclust(res.dist, method = "ward.D2")
dend1 <- as.dendrogram (hc1)
dend2 <- as.dendrogram (hc2)
dend_list <- dendlist(dend1, dend2)
tanglegram(dend1, dend2)
#circular dendrogram
library(factoextra)
inc_clear_dist <- dist(inc_wider, method = "euclidean")
as.matrix(inc_clear_dist)[1:6,1:6]
## 1 2 3 4 5 6
## 1 0.000 15159.373 12435.4668 5099.569 13018.0755 13128.3082
## 2 15159.373 0.000 2723.9289 20258.927 2141.3549 2031.1273
## 3 12435.467 2723.929 0.0000 17535.013 582.6445 692.8921
## 4 5099.569 20258.927 17535.0127 0.000 18117.6165 18227.8495
## 5 13018.075 2141.355 582.6445 18117.616 0.0000 110.2964
## 6 13128.308 2031.127 692.8921 18227.849 110.2964 0.0000
inc_clear_hc <- hclust(d = inc_clear_dist,
method = "ward.D2")
fviz_dend(inc_clear_hc, cex = 0.5, k = 4, k_colors = "jco", type = "circular")
## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.
#phylogenic trees
library("igraph")
## Warning: пакет 'igraph' был собран под R версии 4.2.2
##
## Присоединяю пакет: 'igraph'
## Следующие объекты скрыты от 'package:dplyr':
##
## as_data_frame, groups, union
## Следующие объекты скрыты от 'package:purrr':
##
## compose, simplify
## Следующий объект скрыт от 'package:tidyr':
##
## crossing
## Следующий объект скрыт от 'package:tibble':
##
## as_data_frame
## Следующий объект скрыт от 'package:plotly':
##
## groups
## Следующие объекты скрыты от 'package:stats':
##
## decompose, spectrum
## Следующий объект скрыт от 'package:base':
##
## union
fviz_dend(inc_clear_hc, k = 4, k_colors = "jco",
type = "phylogenic", repel = TRUE)
## Warning: ggrepel: 1329 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Задание 8. Сделайте одновременный график heatmap и
иерархической кластеризации
library(pheatmap)
inc_clear_scaled <- scale(inc_clear)
pheatmap(inc_clear_scaled)
Задание 9. Проведите анализ данных полученных в
задании 5 методом PCA. Кратко проинтерпретируйте полученные
результаты.
inc_pca <- inc_wider %>%
select(age, bmi)
ggplot() +
geom_point(data = inc_pca, aes(x = age, y = bmi)) +
theme_minimal()
inc_pca <- inc_pca %>%
mutate(pc1 = age + bmi)
inc_pca <- inc_pca %>%
mutate(pc1_decile = ntile(pc1, 10))
inc_pca <- inc_pca %>%
mutate(pc1_decile_ch = case_when(
pc1_decile == 1 ~ "1",
(pc1_decile == 5) | (pc1_decile == 6) ~ "5-6",
pc1_decile == 10 ~ "10"
))
inc_pca
## age bmi pc1 pc1_decile pc1_decile_ch
## 1 19 27.900 46.900 1 1
## 2 18 33.770 51.770 2 <NA>
## 3 28 33.000 61.000 4 <NA>
## 4 33 22.705 55.705 3 <NA>
## 5 32 28.880 60.880 4 <NA>
## 6 31 25.740 56.740 3 <NA>
## 7 46 33.440 79.440 7 <NA>
## 8 37 27.740 64.740 4 <NA>
## 9 37 29.830 66.830 5 5-6
## 10 60 25.840 85.840 9 <NA>
## 11 25 26.220 51.220 2 <NA>
## 12 62 26.290 88.290 9 <NA>
## 13 23 34.400 57.400 3 <NA>
## 14 56 39.820 95.820 10 10
## 15 27 42.130 69.130 5 5-6
## 16 19 24.600 43.600 1 1
## 17 52 30.780 82.780 8 <NA>
## 18 23 23.845 46.845 1 1
## 19 56 40.300 96.300 10 10
## 20 30 35.300 65.300 5 5-6
## 21 60 36.005 96.005 10 10
## 22 30 32.400 62.400 4 <NA>
## 23 18 34.100 52.100 2 <NA>
## 24 34 31.920 65.920 5 5-6
## 25 37 28.025 65.025 5 5-6
## 26 59 27.720 86.720 9 <NA>
## 27 63 23.085 86.085 9 <NA>
## 28 55 32.775 87.775 9 <NA>
## 29 23 17.385 40.385 1 1
## 30 31 36.300 67.300 5 5-6
## 31 22 35.600 57.600 3 <NA>
## 32 18 26.315 44.315 1 1
## 33 19 28.600 47.600 1 1
## 34 63 28.310 91.310 10 10
## 35 28 36.400 64.400 4 <NA>
## 36 19 20.425 39.425 1 1
## 37 62 32.965 94.965 10 10
## 38 26 20.800 46.800 1 1
## 39 35 36.670 71.670 6 5-6
## 40 60 39.900 99.900 10 10
## 41 24 26.600 50.600 2 <NA>
## 42 31 36.630 67.630 5 5-6
## 43 41 21.780 62.780 4 <NA>
## 44 37 30.800 67.800 5 5-6
## 45 38 37.050 75.050 7 <NA>
## 46 55 37.300 92.300 10 10
## 47 18 38.665 56.665 3 <NA>
## 48 28 34.770 62.770 4 <NA>
## 49 60 24.530 84.530 8 <NA>
## 50 36 35.200 71.200 6 5-6
## 51 18 35.625 53.625 2 <NA>
## 52 21 33.630 54.630 3 <NA>
## 53 48 28.000 76.000 7 <NA>
## 54 36 34.430 70.430 6 5-6
## 55 40 28.690 68.690 5 5-6
## 56 58 36.955 94.955 10 10
## 57 58 31.825 89.825 9 <NA>
## 58 18 31.680 49.680 2 <NA>
## 59 53 22.880 75.880 7 <NA>
## 60 34 37.335 71.335 6 5-6
## 61 43 27.360 70.360 6 5-6
## 62 25 33.660 58.660 3 <NA>
## 63 64 24.700 88.700 9 <NA>
## 64 28 25.935 53.935 2 <NA>
## 65 20 22.420 42.420 1 1
## 66 19 28.900 47.900 1 1
## 67 61 39.100 100.100 10 10
## 68 40 26.315 66.315 5 5-6
## 69 40 36.190 76.190 7 <NA>
## 70 28 23.980 51.980 2 <NA>
## 71 27 24.750 51.750 2 <NA>
## 72 31 28.500 59.500 4 <NA>
## 73 53 28.100 81.100 8 <NA>
## 74 58 32.010 90.010 9 <NA>
## 75 44 27.400 71.400 6 5-6
## 76 57 34.010 91.010 9 <NA>
## 77 29 29.590 58.590 3 <NA>
## 78 21 35.530 56.530 3 <NA>
## 79 22 39.805 61.805 4 <NA>
## 80 41 32.965 73.965 6 5-6
## 81 31 26.885 57.885 3 <NA>
## 82 45 38.285 83.285 8 <NA>
## 83 22 37.620 59.620 4 <NA>
## 84 48 41.230 89.230 9 <NA>
## 85 37 34.800 71.800 6 5-6
## 86 45 22.895 67.895 5 5-6
## 87 57 31.160 88.160 9 <NA>
## 88 56 27.200 83.200 8 <NA>
## 89 46 27.740 73.740 6 5-6
## 90 55 26.980 81.980 8 <NA>
## 91 21 39.490 60.490 4 <NA>
## 92 53 24.795 77.795 7 <NA>
## 93 59 29.830 88.830 9 <NA>
## 94 35 34.770 69.770 6 5-6
## 95 64 31.300 95.300 10 10
## 96 28 37.620 65.620 5 5-6
## 97 54 30.800 84.800 8 <NA>
## 98 55 38.280 93.280 10 10
## 99 56 19.950 75.950 7 <NA>
## 100 38 19.300 57.300 3 <NA>
## 101 41 31.600 72.600 6 5-6
## 102 30 25.460 55.460 3 <NA>
## 103 18 30.115 48.115 1 1
## 104 61 29.920 90.920 9 <NA>
## 105 34 27.500 61.500 4 <NA>
## 106 20 28.025 48.025 1 1
## 107 19 28.400 47.400 1 1
## 108 26 30.875 56.875 3 <NA>
## 109 29 27.940 56.940 3 <NA>
## 110 63 35.090 98.090 10 10
## 111 54 33.630 87.630 9 <NA>
## 112 55 29.700 84.700 8 <NA>
## 113 37 30.800 67.800 5 5-6
## 114 21 35.720 56.720 3 <NA>
## 115 52 32.205 84.205 8 <NA>
## 116 60 28.595 88.595 9 <NA>
## 117 58 49.060 107.060 10 10
## 118 29 27.940 56.940 3 <NA>
## 119 49 27.170 76.170 7 <NA>
## 120 37 23.370 60.370 4 <NA>
## 121 44 37.100 81.100 8 <NA>
## 122 18 23.750 41.750 1 1
## 123 20 28.975 48.975 1 1
## 124 44 31.350 75.350 7 <NA>
## 125 47 33.915 80.915 8 <NA>
## 126 26 28.785 54.785 3 <NA>
## 127 19 28.300 47.300 1 1
## 128 52 37.400 89.400 9 <NA>
## 129 32 17.765 49.765 2 <NA>
## 130 38 34.700 72.700 6 5-6
## 131 59 26.505 85.505 9 <NA>
## 132 61 22.040 83.040 8 <NA>
## 133 53 35.900 88.900 9 <NA>
## 134 19 25.555 44.555 1 1
## 135 20 28.785 48.785 1 1
## 136 22 28.050 50.050 2 <NA>
## 137 19 34.100 53.100 2 <NA>
## 138 22 25.175 47.175 1 1
## 139 54 31.900 85.900 9 <NA>
## 140 22 36.000 58.000 3 <NA>
## 141 34 22.420 56.420 3 <NA>
## 142 26 32.490 58.490 3 <NA>
## 143 34 25.300 59.300 4 <NA>
## 144 29 29.735 58.735 3 <NA>
## 145 30 28.690 58.690 3 <NA>
## 146 29 38.830 67.830 5 5-6
## 147 46 30.495 76.495 7 <NA>
## 148 51 37.730 88.730 9 <NA>
## 149 53 37.430 90.430 9 <NA>
## 150 19 28.400 47.400 1 1
## 151 35 24.130 59.130 4 <NA>
## 152 48 29.700 77.700 7 <NA>
## 153 32 37.145 69.145 5 5-6
## 154 42 23.370 65.370 5 5-6
## 155 40 25.460 65.460 5 5-6
## 156 44 39.520 83.520 8 <NA>
## 157 48 24.420 72.420 6 5-6
## 158 18 25.175 43.175 1 1
## 159 30 35.530 65.530 5 5-6
## 160 50 27.830 77.830 7 <NA>
## 161 42 26.600 68.600 5 5-6
## 162 18 36.850 54.850 3 <NA>
## 163 54 39.600 93.600 10 10
## 164 32 29.800 61.800 4 <NA>
## 165 37 29.640 66.640 5 5-6
## 166 47 28.215 75.215 7 <NA>
## 167 20 37.000 57.000 3 <NA>
## 168 32 33.155 65.155 5 5-6
## 169 19 31.825 50.825 2 <NA>
## 170 27 18.905 45.905 1 1
## 171 63 41.470 104.470 10 10
## 172 49 30.300 79.300 7 <NA>
## 173 18 15.960 33.960 1 1
## 174 35 34.800 69.800 6 5-6
## 175 24 33.345 57.345 3 <NA>
## 176 63 37.700 100.700 10 10
## 177 38 27.835 65.835 5 5-6
## 178 54 29.200 83.200 8 <NA>
## 179 46 28.900 74.900 6 5-6
## 180 41 33.155 74.155 6 5-6
## 181 58 28.595 86.595 9 <NA>
## 182 18 38.280 56.280 3 <NA>
## 183 22 19.950 41.950 1 1
## 184 44 26.410 70.410 6 5-6
## 185 44 30.690 74.690 6 5-6
## 186 36 41.895 77.895 7 <NA>
## 187 26 29.920 55.920 3 <NA>
## 188 30 30.900 60.900 4 <NA>
## 189 41 32.200 73.200 6 5-6
## 190 29 32.110 61.110 4 <NA>
## 191 61 31.570 92.570 10 10
## 192 36 26.200 62.200 4 <NA>
## 193 25 25.740 50.740 2 <NA>
## 194 56 26.600 82.600 8 <NA>
## 195 18 34.430 52.430 2 <NA>
## 196 19 30.590 49.590 2 <NA>
## 197 39 32.800 71.800 6 5-6
## 198 45 28.600 73.600 6 5-6
## 199 51 18.050 69.050 5 5-6
## 200 64 39.330 103.330 10 10
## 201 19 32.110 51.110 2 <NA>
## 202 48 32.230 80.230 8 <NA>
## 203 60 24.035 84.035 8 <NA>
## 204 27 36.080 63.080 4 <NA>
## 205 46 22.300 68.300 5 5-6
## 206 28 28.880 56.880 3 <NA>
## 207 59 26.400 85.400 9 <NA>
## 208 35 27.740 62.740 4 <NA>
## 209 63 31.800 94.800 10 10
## 210 40 41.230 81.230 8 <NA>
## 211 20 33.000 53.000 2 <NA>
## 212 40 30.875 70.875 6 5-6
## 213 24 28.500 52.500 2 <NA>
## 214 34 26.730 60.730 4 <NA>
## 215 45 30.900 75.900 7 <NA>
## 216 41 37.100 78.100 7 <NA>
## 217 53 26.600 79.600 7 <NA>
## 218 27 23.100 50.100 2 <NA>
## 219 26 29.920 55.920 3 <NA>
## 220 24 23.210 47.210 1 1
## 221 34 33.700 67.700 5 5-6
## 222 53 33.250 86.250 9 <NA>
## 223 32 30.800 62.800 4 <NA>
## 224 19 34.800 53.800 2 <NA>
## 225 42 24.640 66.640 5 5-6
## 226 55 33.880 88.880 9 <NA>
## 227 28 38.060 66.060 5 5-6
## 228 58 41.910 99.910 10 10
## 229 41 31.635 72.635 6 5-6
## 230 47 25.460 72.460 6 5-6
## 231 42 36.195 78.195 7 <NA>
## 232 59 27.830 86.830 9 <NA>
## 233 19 17.800 36.800 1 1
## 234 59 27.500 86.500 9 <NA>
## 235 39 24.510 63.510 4 <NA>
## 236 40 22.220 62.220 4 <NA>
## 237 18 26.730 44.730 1 1
## 238 31 38.390 69.390 5 5-6
## 239 19 29.070 48.070 1 1
## 240 44 38.060 82.060 8 <NA>
## 241 23 36.670 59.670 4 <NA>
## 242 33 22.135 55.135 3 <NA>
## 243 55 26.800 81.800 8 <NA>
## 244 40 35.300 75.300 7 <NA>
## 245 63 27.740 90.740 9 <NA>
## 246 54 30.020 84.020 8 <NA>
## 247 60 38.060 98.060 10 10
## 248 24 35.860 59.860 4 <NA>
## 249 19 20.900 39.900 1 1
## 250 29 28.975 57.975 3 <NA>
## 251 18 17.290 35.290 1 1
## 252 63 32.200 95.200 10 10
## 253 54 34.210 88.210 9 <NA>
## 254 27 30.300 57.300 3 <NA>
## 255 50 31.825 81.825 8 <NA>
## 256 55 25.365 80.365 8 <NA>
## 257 56 33.630 89.630 9 <NA>
## 258 38 40.150 78.150 7 <NA>
## 259 51 24.415 75.415 7 <NA>
## 260 19 31.920 50.920 2 <NA>
## 261 58 25.200 83.200 8 <NA>
## 262 20 26.840 46.840 1 1
## 263 52 24.320 76.320 7 <NA>
## 264 19 36.955 55.955 3 <NA>
## 265 53 38.060 91.060 9 <NA>
## 266 46 42.350 88.350 9 <NA>
## 267 40 19.800 59.800 4 <NA>
## 268 59 32.395 91.395 10 10
## 269 45 30.200 75.200 7 <NA>
## 270 49 25.840 74.840 6 5-6
## 271 18 29.370 47.370 1 1
## 272 50 34.200 84.200 8 <NA>
## 273 41 37.050 78.050 7 <NA>
## 274 50 27.455 77.455 7 <NA>
## 275 25 27.550 52.550 2 <NA>
## 276 47 26.600 73.600 6 5-6
## 277 19 20.615 39.615 1 1
## 278 22 24.300 46.300 1 1
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## 823 18 31.130 49.130 2 <NA>
## 824 44 29.810 73.810 6 5-6
## 825 60 24.320 84.320 8 <NA>
## 826 64 31.825 95.825 10 10
## 827 56 31.790 87.790 9 <NA>
## 828 36 28.025 64.025 4 <NA>
## 829 41 30.780 71.780 6 5-6
## 830 39 21.850 60.850 4 <NA>
## 831 63 33.100 96.100 10 10
## 832 36 25.840 61.840 4 <NA>
## 833 28 23.845 51.845 2 <NA>
## 834 58 34.390 92.390 10 10
## 835 36 33.820 69.820 6 5-6
## 836 42 35.970 77.970 7 <NA>
## 837 36 31.500 67.500 5 5-6
## 838 56 28.310 84.310 8 <NA>
## 839 35 23.465 58.465 3 <NA>
## 840 59 31.350 90.350 9 <NA>
## 841 21 31.100 52.100 2 <NA>
## 842 59 24.700 83.700 8 <NA>
## 843 23 32.780 55.780 3 <NA>
## 844 57 29.810 86.810 9 <NA>
## 845 53 30.495 83.495 8 <NA>
## 846 60 32.450 92.450 10 10
## 847 51 34.200 85.200 8 <NA>
## 848 23 50.380 73.380 6 5-6
## 849 27 24.100 51.100 2 <NA>
## 850 55 32.775 87.775 9 <NA>
## 851 37 30.780 67.780 5 5-6
## 852 61 32.300 93.300 10 10
## 853 46 35.530 81.530 8 <NA>
## 854 53 23.750 76.750 7 <NA>
## 855 49 23.845 72.845 6 5-6
## 856 20 29.600 49.600 2 <NA>
## 857 48 33.110 81.110 8 <NA>
## 858 25 24.130 49.130 2 <NA>
## 859 25 32.230 57.230 3 <NA>
## 860 57 28.100 85.100 8 <NA>
## 861 37 47.600 84.600 8 <NA>
## 862 38 28.000 66.000 5 5-6
## 863 55 33.535 88.535 9 <NA>
## 864 36 19.855 55.855 3 <NA>
## 865 51 25.400 76.400 7 <NA>
## 866 40 29.900 69.900 6 5-6
## 867 18 37.290 55.290 3 <NA>
## 868 57 43.700 100.700 10 10
## 869 61 23.655 84.655 8 <NA>
## 870 25 24.300 49.300 2 <NA>
## 871 50 36.200 86.200 9 <NA>
## 872 26 29.480 55.480 3 <NA>
## 873 42 24.860 66.860 5 5-6
## 874 43 30.100 73.100 6 5-6
## 875 44 21.850 65.850 5 5-6
## 876 23 28.120 51.120 2 <NA>
## 877 49 27.100 76.100 7 <NA>
## 878 33 33.440 66.440 5 5-6
## 879 41 28.800 69.800 6 5-6
## 880 37 29.500 66.500 5 5-6
## 881 22 34.800 56.800 3 <NA>
## 882 23 27.360 50.360 2 <NA>
## 883 21 22.135 43.135 1 1
## 884 51 37.050 88.050 9 <NA>
## 885 25 26.695 51.695 2 <NA>
## 886 32 28.930 60.930 4 <NA>
## 887 57 28.975 85.975 9 <NA>
## 888 36 30.020 66.020 5 5-6
## 889 22 39.500 61.500 4 <NA>
## 890 57 33.630 90.630 9 <NA>
## 891 64 26.885 90.885 9 <NA>
## 892 36 29.040 65.040 5 5-6
## 893 54 24.035 78.035 7 <NA>
## 894 47 38.940 85.940 9 <NA>
## 895 62 32.110 94.110 10 10
## 896 61 44.000 105.000 10 10
## 897 43 20.045 63.045 4 <NA>
## 898 19 25.555 44.555 1 1
## 899 18 40.260 58.260 3 <NA>
## 900 19 22.515 41.515 1 1
## 901 49 22.515 71.515 6 5-6
## 902 60 40.920 100.920 10 10
## 903 26 27.265 53.265 2 <NA>
## 904 49 36.850 85.850 9 <NA>
## 905 60 35.100 95.100 10 10
## 906 26 29.355 55.355 3 <NA>
## 907 27 32.585 59.585 4 <NA>
## 908 44 32.340 76.340 7 <NA>
## 909 63 39.800 102.800 10 10
## 910 32 24.600 56.600 3 <NA>
## 911 22 28.310 50.310 2 <NA>
## 912 18 31.730 49.730 2 <NA>
## 913 59 26.695 85.695 9 <NA>
## 914 44 27.500 71.500 6 5-6
## 915 33 24.605 57.605 3 <NA>
## 916 24 33.990 57.990 3 <NA>
## 917 43 26.885 69.885 6 5-6
## 918 45 22.895 67.895 5 5-6
## 919 61 28.200 89.200 9 <NA>
## 920 35 34.210 69.210 5 5-6
## 921 62 25.000 87.000 9 <NA>
## 922 62 33.200 95.200 10 10
## 923 38 31.000 69.000 5 5-6
## 924 34 35.815 69.815 6 5-6
## 925 43 23.200 66.200 5 5-6
## 926 50 32.110 82.110 8 <NA>
## 927 19 23.400 42.400 1 1
## 928 57 20.100 77.100 7 <NA>
## 929 62 39.160 101.160 10 10
## 930 41 34.210 75.210 7 <NA>
## 931 26 46.530 72.530 6 5-6
## 932 39 32.500 71.500 6 5-6
## 933 46 25.800 71.800 6 5-6
## 934 45 35.300 80.300 8 <NA>
## 935 32 37.180 69.180 5 5-6
## 936 59 27.500 86.500 9 <NA>
## 937 44 29.735 73.735 6 5-6
## 938 39 24.225 63.225 4 <NA>
## 939 18 26.180 44.180 1 1
## 940 53 29.480 82.480 8 <NA>
## 941 18 23.210 41.210 1 1
## 942 50 46.090 96.090 10 10
## 943 18 40.185 58.185 3 <NA>
## 944 19 22.610 41.610 1 1
## 945 62 39.930 101.930 10 10
## 946 56 35.800 91.800 10 10
## 947 42 35.800 77.800 7 <NA>
## 948 37 34.200 71.200 6 5-6
## 949 42 31.255 73.255 6 5-6
## 950 25 29.700 54.700 3 <NA>
## 951 57 18.335 75.335 7 <NA>
## 952 51 42.900 93.900 10 10
## 953 30 28.405 58.405 3 <NA>
## 954 44 30.200 74.200 6 5-6
## 955 34 27.835 61.835 4 <NA>
## 956 31 39.490 70.490 6 5-6
## 957 54 30.800 84.800 8 <NA>
## 958 24 26.790 50.790 2 <NA>
## 959 43 34.960 77.960 7 <NA>
## 960 48 36.670 84.670 8 <NA>
## 961 19 39.615 58.615 3 <NA>
## 962 29 25.900 54.900 3 <NA>
## 963 63 35.200 98.200 10 10
## 964 46 24.795 70.795 6 5-6
## 965 52 36.765 88.765 9 <NA>
## 966 35 27.100 62.100 4 <NA>
## 967 51 24.795 75.795 7 <NA>
## 968 44 25.365 69.365 5 5-6
## 969 21 25.745 46.745 1 1
## 970 39 34.320 73.320 6 5-6
## 971 50 28.160 78.160 7 <NA>
## 972 34 23.560 57.560 3 <NA>
## 973 22 20.235 42.235 1 1
## 974 19 40.500 59.500 4 <NA>
## 975 26 35.420 61.420 4 <NA>
## 976 29 22.895 51.895 2 <NA>
## 977 48 40.150 88.150 9 <NA>
## 978 26 29.150 55.150 3 <NA>
## 979 45 39.995 84.995 8 <NA>
## 980 36 29.920 65.920 5 5-6
## 981 54 25.460 79.460 7 <NA>
## 982 34 21.375 55.375 3 <NA>
## 983 31 25.900 56.900 3 <NA>
## 984 27 30.590 57.590 3 <NA>
## 985 20 30.115 50.115 2 <NA>
## 986 44 25.800 69.800 6 5-6
## 987 43 30.115 73.115 6 5-6
## 988 45 27.645 72.645 6 5-6
## 989 34 34.675 68.675 5 5-6
## 990 24 20.520 44.520 1 1
## 991 26 19.800 45.800 1 1
## 992 38 27.835 65.835 5 5-6
## 993 50 31.600 81.600 8 <NA>
## 994 38 28.270 66.270 5 5-6
## 995 27 20.045 47.045 1 1
## 996 39 23.275 62.275 4 <NA>
## 997 39 34.100 73.100 6 5-6
## 998 63 36.850 99.850 10 10
## 999 33 36.290 69.290 5 5-6
## 1000 36 26.885 62.885 4 <NA>
## 1001 30 22.990 52.990 2 <NA>
## 1002 24 32.700 56.700 3 <NA>
## 1003 24 25.800 49.800 2 <NA>
## 1004 48 29.600 77.600 7 <NA>
## 1005 47 19.190 66.190 5 5-6
## 1006 29 31.730 60.730 4 <NA>
## 1007 28 29.260 57.260 3 <NA>
## 1008 47 28.215 75.215 7 <NA>
## 1009 25 24.985 49.985 2 <NA>
## 1010 51 27.740 78.740 7 <NA>
## 1011 48 22.800 70.800 6 5-6
## 1012 43 20.130 63.130 4 <NA>
## 1013 61 33.330 94.330 10 10
## 1014 48 32.300 80.300 8 <NA>
## 1015 38 27.600 65.600 5 5-6
## 1016 59 25.460 84.460 8 <NA>
## 1017 19 24.605 43.605 1 1
## 1018 26 34.200 60.200 4 <NA>
## 1019 54 35.815 89.815 9 <NA>
## 1020 21 32.680 53.680 2 <NA>
## 1021 51 37.000 88.000 9 <NA>
## 1022 22 31.020 53.020 2 <NA>
## 1023 47 36.080 83.080 8 <NA>
## 1024 18 23.320 41.320 1 1
## 1025 47 45.320 92.320 10 10
## 1026 21 34.600 55.600 3 <NA>
## 1027 19 26.030 45.030 1 1
## 1028 23 18.715 41.715 1 1
## 1029 54 31.600 85.600 9 <NA>
## 1030 37 17.290 54.290 2 <NA>
## 1031 46 23.655 69.655 6 5-6
## 1032 55 35.200 90.200 9 <NA>
## 1033 30 27.930 57.930 3 <NA>
## 1034 18 21.565 39.565 1 1
## 1035 61 38.380 99.380 10 10
## 1036 54 23.000 77.000 7 <NA>
## 1037 22 37.070 59.070 4 <NA>
## 1038 45 30.495 75.495 7 <NA>
## 1039 22 28.880 50.880 2 <NA>
## 1040 19 27.265 46.265 1 1
## 1041 35 28.025 63.025 4 <NA>
## 1042 18 23.085 41.085 1 1
## 1043 20 30.685 50.685 2 <NA>
## 1044 28 25.800 53.800 2 <NA>
## 1045 55 35.245 90.245 9 <NA>
## 1046 43 24.700 67.700 5 5-6
## 1047 43 25.080 68.080 5 5-6
## 1048 22 52.580 74.580 6 5-6
## 1049 25 22.515 47.515 1 1
## 1050 49 30.900 79.900 7 <NA>
## 1051 44 36.955 80.955 8 <NA>
## 1052 64 26.410 90.410 9 <NA>
## 1053 49 29.830 78.830 7 <NA>
## 1054 47 29.800 76.800 7 <NA>
## 1055 27 21.470 48.470 1 1
## 1056 55 27.645 82.645 8 <NA>
## 1057 48 28.900 76.900 7 <NA>
## 1058 45 31.790 76.790 7 <NA>
## 1059 24 39.490 63.490 4 <NA>
## 1060 32 33.820 65.820 5 5-6
## 1061 24 32.010 56.010 3 <NA>
## 1062 57 27.940 84.940 8 <NA>
## 1063 59 41.140 100.140 10 10
## 1064 36 28.595 64.595 4 <NA>
## 1065 29 25.600 54.600 3 <NA>
## 1066 42 25.300 67.300 5 5-6
## 1067 48 37.290 85.290 9 <NA>
## 1068 39 42.655 81.655 8 <NA>
## 1069 63 21.660 84.660 8 <NA>
## 1070 54 31.900 85.900 9 <NA>
## 1071 37 37.070 74.070 6 5-6
## 1072 63 31.445 94.445 10 10
## 1073 21 31.255 52.255 2 <NA>
## 1074 54 28.880 82.880 8 <NA>
## 1075 60 18.335 78.335 7 <NA>
## 1076 32 29.590 61.590 4 <NA>
## 1077 47 32.000 79.000 7 <NA>
## 1078 21 26.030 47.030 1 1
## 1079 28 31.680 59.680 4 <NA>
## 1080 63 33.660 96.660 10 10
## 1081 18 21.780 39.780 1 1
## 1082 32 27.835 59.835 4 <NA>
## 1083 38 19.950 57.950 3 <NA>
## 1084 32 31.500 63.500 4 <NA>
## 1085 62 30.495 92.495 10 10
## 1086 39 18.300 57.300 3 <NA>
## 1087 55 28.975 83.975 8 <NA>
## 1088 57 31.540 88.540 9 <NA>
## 1089 52 47.740 99.740 10 10
## 1090 56 22.100 78.100 7 <NA>
## 1091 47 36.190 83.190 8 <NA>
## 1092 55 29.830 84.830 8 <NA>
## 1093 23 32.700 55.700 3 <NA>
## 1094 22 30.400 52.400 2 <NA>
## 1095 50 33.700 83.700 8 <NA>
## 1096 18 31.350 49.350 2 <NA>
## 1097 51 34.960 85.960 9 <NA>
## 1098 22 33.770 55.770 3 <NA>
## 1099 52 30.875 82.875 8 <NA>
## 1100 25 33.990 58.990 3 <NA>
## 1101 33 19.095 52.095 2 <NA>
## 1102 53 28.600 81.600 8 <NA>
## 1103 29 38.940 67.940 5 5-6
## 1104 58 36.080 94.080 10 10
## 1105 37 29.800 66.800 5 5-6
## 1106 54 31.240 85.240 8 <NA>
## 1107 49 29.925 78.925 7 <NA>
## 1108 50 26.220 76.220 7 <NA>
## 1109 26 30.000 56.000 3 <NA>
## 1110 45 20.350 65.350 5 5-6
## 1111 54 32.300 86.300 9 <NA>
## 1112 38 38.390 76.390 7 <NA>
## 1113 48 25.850 73.850 6 5-6
## 1114 28 26.315 54.315 2 <NA>
## 1115 23 24.510 47.510 1 1
## 1116 55 32.670 87.670 9 <NA>
## 1117 41 29.640 70.640 6 5-6
## 1118 25 33.330 58.330 3 <NA>
## 1119 33 35.750 68.750 5 5-6
## 1120 30 19.950 49.950 2 <NA>
## 1121 23 31.400 54.400 3 <NA>
## 1122 46 38.170 84.170 8 <NA>
## 1123 53 36.860 89.860 9 <NA>
## 1124 27 32.395 59.395 4 <NA>
## 1125 23 42.750 65.750 5 5-6
## 1126 63 25.080 88.080 9 <NA>
## 1127 55 29.900 84.900 8 <NA>
## 1128 35 35.860 70.860 6 5-6
## 1129 34 32.800 66.800 5 5-6
## 1130 19 18.600 37.600 1 1
## 1131 39 23.870 62.870 4 <NA>
## 1132 27 45.900 72.900 6 5-6
## 1133 57 40.280 97.280 10 10
## 1134 52 18.335 70.335 6 5-6
## 1135 28 33.820 61.820 4 <NA>
## 1136 50 28.120 78.120 7 <NA>
## 1137 44 25.000 69.000 5 5-6
## 1138 26 22.230 48.230 1 1
## 1139 33 30.250 63.250 4 <NA>
## 1140 19 32.490 51.490 2 <NA>
## 1141 50 37.070 87.070 9 <NA>
## 1142 41 32.600 73.600 6 5-6
## 1143 52 24.860 76.860 7 <NA>
## 1144 39 32.340 71.340 6 5-6
## 1145 50 32.300 82.300 8 <NA>
## 1146 52 32.775 84.775 8 <NA>
## 1147 60 32.800 92.800 10 10
## 1148 20 31.920 51.920 2 <NA>
## 1149 55 21.500 76.500 7 <NA>
## 1150 42 34.100 76.100 7 <NA>
## 1151 18 30.305 48.305 1 1
## 1152 58 36.480 94.480 10 10
## 1153 43 32.560 75.560 7 <NA>
## 1154 35 35.815 70.815 6 5-6
## 1155 48 27.930 75.930 7 <NA>
## 1156 36 22.135 58.135 3 <NA>
## 1157 19 44.880 63.880 4 <NA>
## 1158 23 23.180 46.180 1 1
## 1159 20 30.590 50.590 2 <NA>
## 1160 32 41.100 73.100 6 5-6
## 1161 43 34.580 77.580 7 <NA>
## 1162 34 42.130 76.130 7 <NA>
## 1163 30 38.830 68.830 5 5-6
## 1164 18 28.215 46.215 1 1
## 1165 41 28.310 69.310 5 5-6
## 1166 35 26.125 61.125 4 <NA>
## 1167 57 40.370 97.370 10 10
## 1168 29 24.600 53.600 2 <NA>
## 1169 32 35.200 67.200 5 5-6
## 1170 37 34.105 71.105 6 5-6
## 1171 18 27.360 45.360 1 1
## 1172 43 26.700 69.700 6 5-6
## 1173 56 41.910 97.910 10 10
## 1174 38 29.260 67.260 5 5-6
## 1175 29 32.110 61.110 4 <NA>
## 1176 22 27.100 49.100 1 1
## 1177 52 24.130 76.130 7 <NA>
## 1178 40 27.400 67.400 5 5-6
## 1179 23 34.865 57.865 3 <NA>
## 1180 31 29.810 60.810 4 <NA>
## 1181 42 41.325 83.325 8 <NA>
## 1182 24 29.925 53.925 2 <NA>
## 1183 25 30.300 55.300 3 <NA>
## 1184 48 27.360 75.360 7 <NA>
## 1185 23 28.490 51.490 2 <NA>
## 1186 45 23.560 68.560 5 5-6
## 1187 20 35.625 55.625 3 <NA>
## 1188 62 32.680 94.680 10 10
## 1189 43 25.270 68.270 5 5-6
## 1190 23 28.000 51.000 2 <NA>
## 1191 31 32.775 63.775 4 <NA>
## 1192 41 21.755 62.755 4 <NA>
## 1193 58 32.395 90.395 9 <NA>
## 1194 48 36.575 84.575 8 <NA>
## 1195 31 21.755 52.755 2 <NA>
## 1196 19 27.930 46.930 1 1
## 1197 19 30.020 49.020 1 1
## 1198 41 33.550 74.550 6 5-6
## 1199 40 29.355 69.355 5 5-6
## 1200 31 25.800 56.800 3 <NA>
## 1201 37 24.320 61.320 4 <NA>
## 1202 46 40.375 86.375 9 <NA>
## 1203 22 32.110 54.110 2 <NA>
## 1204 51 32.300 83.300 8 <NA>
## 1205 18 27.280 45.280 1 1
## 1206 35 17.860 52.860 2 <NA>
## 1207 59 34.800 93.800 10 10
## 1208 36 33.400 69.400 5 5-6
## 1209 37 25.555 62.555 4 <NA>
## 1210 59 37.100 96.100 10 10
## 1211 36 30.875 66.875 5 5-6
## 1212 39 34.100 73.100 6 5-6
## 1213 18 21.470 39.470 1 1
## 1214 52 33.300 85.300 9 <NA>
## 1215 27 31.255 58.255 3 <NA>
## 1216 18 39.140 57.140 3 <NA>
## 1217 40 25.080 65.080 5 5-6
## 1218 29 37.290 66.290 5 5-6
## 1219 46 34.600 80.600 8 <NA>
## 1220 38 30.210 68.210 5 5-6
## 1221 30 21.945 51.945 2 <NA>
## 1222 40 24.970 64.970 5 5-6
## 1223 50 25.300 75.300 7 <NA>
## 1224 20 24.420 44.420 1 1
## 1225 41 23.940 64.940 5 5-6
## 1226 33 39.820 72.820 6 5-6
## 1227 38 16.815 54.815 3 <NA>
## 1228 42 37.180 79.180 7 <NA>
## 1229 56 34.430 90.430 9 <NA>
## 1230 58 30.305 88.305 9 <NA>
## 1231 52 34.485 86.485 9 <NA>
## 1232 20 21.800 41.800 1 1
## 1233 54 24.605 78.605 7 <NA>
## 1234 58 23.300 81.300 8 <NA>
## 1235 45 27.830 72.830 6 5-6
## 1236 26 31.065 57.065 3 <NA>
## 1237 63 21.660 84.660 8 <NA>
## 1238 58 28.215 86.215 9 <NA>
## 1239 37 22.705 59.705 4 <NA>
## 1240 25 42.130 67.130 5 5-6
## 1241 52 41.800 93.800 10 10
## 1242 64 36.960 100.960 10 10
## 1243 22 21.280 43.280 1 1
## 1244 28 33.110 61.110 4 <NA>
## 1245 18 33.330 51.330 2 <NA>
## 1246 28 24.300 52.300 2 <NA>
## 1247 45 25.700 70.700 6 5-6
## 1248 33 29.400 62.400 4 <NA>
## 1249 18 39.820 57.820 3 <NA>
## 1250 32 33.630 65.630 5 5-6
## 1251 24 29.830 53.830 2 <NA>
## 1252 19 19.800 38.800 1 1
## 1253 20 27.300 47.300 1 1
## 1254 40 29.300 69.300 5 5-6
## 1255 34 27.720 61.720 4 <NA>
## 1256 42 37.900 79.900 7 <NA>
## 1257 51 36.385 87.385 9 <NA>
## 1258 54 27.645 81.645 8 <NA>
## 1259 55 37.715 92.715 10 10
## 1260 52 23.180 75.180 7 <NA>
## 1261 32 20.520 52.520 2 <NA>
## 1262 28 37.100 65.100 5 5-6
## 1263 41 28.050 69.050 5 5-6
## 1264 43 29.900 72.900 6 5-6
## 1265 49 33.345 82.345 8 <NA>
## 1266 64 23.760 87.760 9 <NA>
## 1267 55 30.500 85.500 9 <NA>
## 1268 24 31.065 55.065 3 <NA>
## 1269 20 33.300 53.300 2 <NA>
## 1270 45 27.500 72.500 6 5-6
## 1271 26 33.915 59.915 4 <NA>
## 1272 25 34.485 59.485 4 <NA>
## 1273 43 25.520 68.520 5 5-6
## 1274 35 27.610 62.610 4 <NA>
## 1275 26 27.060 53.060 2 <NA>
## 1276 57 23.700 80.700 8 <NA>
## 1277 22 30.400 52.400 2 <NA>
## 1278 32 29.735 61.735 4 <NA>
## 1279 39 29.925 68.925 5 5-6
## 1280 25 26.790 51.790 2 <NA>
## 1281 48 33.330 81.330 8 <NA>
## 1282 47 27.645 74.645 6 5-6
## 1283 18 21.660 39.660 1 1
## 1284 18 30.030 48.030 1 1
## 1285 61 36.300 97.300 10 10
## 1286 47 24.320 71.320 6 5-6
## 1287 28 17.290 45.290 1 1
## 1288 36 25.900 61.900 4 <NA>
## 1289 20 39.400 59.400 4 <NA>
## 1290 44 34.320 78.320 7 <NA>
## 1291 38 19.950 57.950 3 <NA>
## 1292 19 34.900 53.900 2 <NA>
## 1293 21 23.210 44.210 1 1
## 1294 46 25.745 71.745 6 5-6
## 1295 58 25.175 83.175 8 <NA>
## 1296 20 22.000 42.000 1 1
## 1297 18 26.125 44.125 1 1
## 1298 28 26.510 54.510 3 <NA>
## 1299 33 27.455 60.455 4 <NA>
## 1300 19 25.745 44.745 1 1
## 1301 45 30.360 75.360 7 <NA>
## 1302 62 30.875 92.875 10 10
## 1303 25 20.800 45.800 1 1
## 1304 43 27.800 70.800 6 5-6
## 1305 42 24.605 66.605 5 5-6
## 1306 24 27.720 51.720 2 <NA>
## 1307 29 21.850 50.850 2 <NA>
## 1308 32 28.120 60.120 4 <NA>
## 1309 25 30.200 55.200 3 <NA>
## 1310 41 32.200 73.200 6 5-6
## 1311 42 26.315 68.315 5 5-6
## 1312 33 26.695 59.695 4 <NA>
## 1313 34 42.900 76.900 7 <NA>
## 1314 19 34.700 53.700 2 <NA>
## 1315 30 23.655 53.655 2 <NA>
## 1316 18 28.310 46.310 1 1
## 1317 19 20.600 39.600 1 1
## 1318 18 53.130 71.130 6 5-6
## 1319 35 39.710 74.710 6 5-6
## 1320 39 26.315 65.315 5 5-6
## 1321 31 31.065 62.065 4 <NA>
## 1322 62 26.695 88.695 9 <NA>
## 1323 62 38.830 100.830 10 10
## 1324 42 40.370 82.370 8 <NA>
## 1325 31 25.935 56.935 3 <NA>
## 1326 61 33.535 94.535 10 10
## 1327 42 32.870 74.870 6 5-6
## 1328 51 30.030 81.030 8 <NA>
## 1329 23 24.225 47.225 1 1
## 1330 52 38.600 90.600 9 <NA>
## 1331 57 25.740 82.740 8 <NA>
## 1332 23 33.400 56.400 3 <NA>
## 1333 52 44.700 96.700 10 10
## 1334 50 30.970 80.970 8 <NA>
## 1335 18 31.920 49.920 2 <NA>
## 1336 18 36.850 54.850 3 <NA>
## 1337 21 25.800 46.800 1 1
## 1338 61 29.070 90.070 9 <NA>
ggplot() +
geom_point(data = inc_pca,
aes(x = age,
y = bmi,
color = pc1_decile_ch)) +
theme_minimal()
inc_pca <- inc_pca %>%
mutate(pc2 = age - bmi) %>%
mutate(pc2_decile = ntile(pc2, 10)) %>%
mutate(pc2_decile_ch = case_when(
pc2_decile == 1 ~ "1",
(pc2_decile == 5) | (pc2_decile == 6) ~ "5-6",
pc2_decile == 10 ~ "10"
))
ggplot() +
geom_point(data = inc_pca,
aes(x = age,
y = bmi,
color = pc2_decile_ch)) +
theme_minimal()
inc_pca_pc1 <- inc_pca %>%
group_by(pc1_decile) %>%
summarise(age_pc1 = mean(age),
bmi_pc1 = mean(bmi))
inc_pca_pc2 <- inc_pca %>%
group_by(pc2_decile) %>%
summarise(age_pc2 = mean(age),
bmi_pc2 = mean(bmi))
inc_pca_ggplot <- ggplot() +
geom_point(data = inc_pca,
aes(x = age, y = bmi)) +
geom_smooth(data = inc_pca_pc1,
aes(x = age_pc1, y = bmi_pc1),
method=lm,
color="Blue", fullrange = F,
size = 2
) +
geom_smooth(data = inc_pca_pc2,
aes(x = age_pc2, y = bmi_pc2),
method=lm,
orientation = "y",
color="Green", fullrange = F,
size = 2
) +
theme_minimal()
inc_pca_ggplot
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
ggplot() +
geom_point(data = inc_pca, aes(x = pc1, y = pc2)) +
theme_minimal()
inc_pca <- inc_pca %>%
select(age, bmi)
inc.pca <- prcomp(inc_pca,
scale = T)
inc.pca$rotation
## PC1 PC2
## age 0.7071068 0.7071068
## bmi 0.7071068 -0.7071068
data <- as.data.frame(inc.pca$x)
ggplot() +
geom_point(data = data,
aes(x = PC1, y = PC2)) +
theme_minimal()
Задание 10. В финале вы получите график PCA по
наблюдениям и переменным. Сделайте кластеризацию данных на нём по
возрастным группам (создайте их сами на ваш вкус, но их количество
должно быть не меньше 3)
inc_pca_ggplot
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
Задание 11. Подумайте и создайте ещё две
номинативные переменные, которые бы гипотетически могли хорошо разбить
данные на кластеры. Сделайте две соответствующие визуализации.
Задание 12. Давайте самостоятельно увидим, что
снижение размерности – это группа методов, славящаяся своей
неустойчивостью. Попробуйте самостоятельно поизменять дафрейм – удалить
какие-либо переменные или создать их (создавайте только дамми
переменные). Ваша задача – резко поднять качество вашего анализа PCA
(при этом, фактически, оперируя всё теми же данными). Кратко опишите,
почему добавление той или иной дамми переменной так улучшает PCA.